Root cause incident detection using an alarm correlation engine

ABSTRACT

In various examples, description data may be used by an engine to correlate a subset of alarms representing a network incident. A machine learning model may then be used to predict a likelihood that one or more of the alarms within the subset is a root cause of the network incident. This root cause may then be displayed on a graphical user interface. As a result, alarm fatigue experienced by network administrators may be reduced.

SUMMARY

The present disclosure is directed, in part, to systems and methods forroot cause detection of an incident using an alarm correlation engine,substantially as shown in and/or described in connection with at leastone of the figures, and as set forth more completely in the claims.

Alarm management systems play a critical role in monitoring the healthof a communications network, such as a telecommunications network. Thesetype of networks are often complex in nature and may generate atremendous number of alarms in any given time period, representingvarious incidents, such as hardware, configuration, or softwarefailures. Network administrators monitoring alarm management systems forthese networks may, in turn, experience alarm overload. For example, theincoming rate of network alarms for any given time period may becomeexcessive so that an administrator may not have the ability to timelyidentify which of the multitude of alarms represents a root cause of anincident as opposed to a symptom of the root cause, which may result infurther degradation of the network.

Disclosed approaches may use an engine to analyze description dataassociated with a set of alarms from a plurality of nodes of acommunication network, representing a plurality of network incidents,and determine a subset of alarms that represent a network incident. Uponidentifying the subset of alarms that represent a network incident, thesubset may be applied to a machine learning model trained to predict alikelihood that one or more alarms of the subset is a root cause of thenetwork incident. One or more alarms of the subset may be selected basedat least on the likelihood that the one or more alarms is the root causeof the network incident. Once the likely root cause of the networkincident has been selected, this root cause may be communicated fordisplay in a graphical user interface. In addition, disclosed approachesmay update parameters of the machine learning model based on the one ormore alarms selected and the one or more root cause predictions usingthe actual determined root cause of the incident in order to furthertrain the machine learning model to accurately predict a root cause ofan incident. In these ways, the mean time for detecting a root cause ofa network incident is significantly reduced, which can prevent moreserious network incidents, such as a network failure. In addition,corresponding operator time and expense in conjunction with analyzingpotential network alarms can be reduced.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used in isolation as an aid in determining the scope of the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the attached figures, which are intendedto be exemplary and non-limiting, wherein:

FIG. 1 depicts an example of a network environment in accordance withembodiments of the present disclosure.

FIG. 2 depicts an example of a network environment in accordance withembodiments of the present disclosure.

FIG. 3 illustrates a plurality of nodes in a network environment inaccordance with embodiments of the present disclosure.

FIG. 4 is an illustration of a graphical user interface depicting amultitude of alarms in a network environment.

FIG. 5 is an illustration of a graphical user interface in a networkenvironment in accordance with some embodiments of the presentdisclosure.

FIG. 6 is a flow diagram showing a method in accordance with someembodiments of the present disclosure.

FIG. 7 depicts an exemplary computing device suitable for use inimplementations of aspects herein.

DETAILED DESCRIPTION

Systems and methods are disclosed related to root cause alarm detectionof an incident using an alarm correlation engine. The subject matter ofembodiments of the invention is described with specificity herein tomeet statutory requirements. However, the description itself is notintended to limit the scope of this patent. The claimed subject mattermight be embodied in other ways to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Terms should notbe interpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

In certain aspects, a method in a communication network is provided. Inembodiments, a set of alarms from a plurality of nodes may be received.In other embodiments, the set of alarms may represent a plurality ofincidents. Using an engine, a first subset of the set of alarmsrepresenting a plurality of incidents may be determined by analyzingdescription data associated with the set of alarms. In embodiments, thefirst subset of the set of alarms may be applied to a machine learningmodel to predict a likelihood that one or more of the first subset ofalarms is a root cause of the incident. One or more alarms of the firstsubset of the set of alarms may be selected based on the likelihood thatsaid one or more alarms is the root cause of the incident. In stillfurther embodiments, the root cause of the incident may be communicatedfor display in a graphical user interface.

In other aspects, a system in a communication network is provided. Thesystem may include one or more processors and one or more computerstorage hardware devices storing computer-usable instructions. Thecomputer-usable instructions may cause the one or more processors toreceive a set of alarms from a plurality of nodes. In embodiments, theset of alarms may represent a plurality of network incidents. In otherembodiments, the computer-usable instructions may cause the one or moreprocessors to determine a first subset of the set of alarms representingan incident by analyzing description data associated with the set ofalarms. In further embodiments, the computer-usable instructions maycause the one or more processors to apply the first subset of the set ofalarms to a machine learning model trained to predict a likelihood thatone or more alarms of the first subset is a root cause of the incident.The computer-usable instructions may further cause the one or moreprocessors to select one or more alarms of the first subset of the setof alarms based at least on the likelihood that said one or more alarmsis the root cause of the incident. In addition, the computer-usableinstructions may cause the one or more processors to communicate theroot cause of the incident for display in a graphical user interface.

In further aspects, a method in a communication network is provided. Inembodiments, a set of alarms from a plurality of nodes may be received.In other embodiments, the set of alarms may represent a plurality ofincidents. Using an engine, a first subset of the set of alarmsrepresenting a plurality of incidents may be determined by analyzingdescription data associated with the set of alarms. In embodiments, thefirst subset of the set of alarms may be applied to a machine learningmodel to generate one or more prediction regarding a root cause of theincident. In further embodiments, one or more of the first subset of theset alarms may be selected based at least on the one or morepredictions. In still further embodiments, parameters of the machinelearning may be updated based on the selecting and the one or morepredictions.

Advantageously, by providing methods and systems in a communicationnetwork that use an alarm correlation engine, a root cause of a networkincident can be more easily identified thereby resulting in asignificant reduction in the mean time for resolving a network problem,and a machine learning model that can more accurately predict one ormore alarms that are a root cause of an incident.

Throughout this disclosure, several acronyms and shorthand notations areused to aid the understanding of certain concepts pertaining to theassociated system and services. These acronyms and shorthand notationsare intended to help provide an easy methodology of communicating theideas expressed herein and are not meant to limit the scope of aspectsherein.

Embodiments herein may be embodied as, among other things: a method,system, or set of instructions embodied on one or more computer-readablemedia. Computer-readable media include both volatile and nonvolatilemedia, removable and nonremovable media, and contemplate media readableby a database, a switch, and various other network devices.Computer-readable media includes media implemented in any way forstoring information. Examples of stored information includecomputer-useable instructions, data structures, program circuitry, andother data representations. Media examples include RAM, ROM, EEPROM,flash memory or other memory technology, CD-ROM, digital versatile discs(DVD), holographic media or other optical disc storage, magneticcassettes, magnetic tape, magnetic disk storage, and other magneticstorage devices. These technologies can store data momentarily,temporarily, or permanently. Embodiments may take the form of a hardwareembodiment, or an embodiment combining software and hardware. Someembodiments may take the form of a computer-program product thatincludes computer-useable or computer-executable instructions embodiedon one or more computer-readable media.

“Computer-readable media” may be any available media and may includevolatile and nonvolatile media, as well as removable and non-removablemedia. By way of example, and not limitation, computer-readable mediamay include computer storage media and communication media.

“Computer storage media” may include, without limitation, volatile andnonvolatile media, as well as removable and non-removable media,implemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program circuitry,or other data. In this regard, computer storage media may include, butis not limited to, Random Access Memory (RAM), Read-Only Memory (ROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), flashmemory or other memory technology, CD-ROM, digital versatile disks(DVDs) or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage, or other magnetic storage devices, or any othermedium which may be used to store the desired information and which maybe accessed by the computing device 700 shown in FIG. 5 . Computerstorage media does not comprise a signal per se.

“Communication media” may include, without limitation, computer-readableinstructions, data structures, program circuitry, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. As usedherein, the term “modulated data signal” refers to a signal that has oneor more of its attributes set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, radiofrequency (RF), infrared, and other wireless media. Combinations of anyof the above also may be included within the scope of computer-readablemedia.

A “network” refers to a network comprised of wireless and wiredcomponents that provide wireless communications service coverage to oneor more UE. The network may comprise one or more base stations, one ormore cell sites (i.e., managed by a base station), one or more celltowers (i.e., having an antenna) associated with each base station orcell site, a gateway, a backhaul server that connects two or more basestations, a database, a power supply, sensors, and other components notdiscussed herein, in various embodiments.

The terms “base station” and “cell site” may be used interchangeablyherein to refer to a defined wireless communications coverage area(i.e., a geographic area) serviced by a base station. It will beunderstood that one base station may control one cell site oralternatively, one base station may control multiple cell sites. Asdiscussed herein, a base station is deployed in the network to controland facilitate, via one or more antenna arrays, the broadcast,transmission, synchronization, and receipt of one or more wirelesssignals in order to communicate with, verify, authenticate, and providewireless communications service coverage to one or more UE that requestto join and/or are connected to a network.

An “access point” may refer to hardware, software, devices, or othercomponents at a base station, cell site, and/or cell tower having anantenna, an antenna array, a radio, a transceiver, and/or a controller.Generally, an access point may communicate directly with user equipmentaccording to one or more access technologies (e.g., 3G, 4G, LTE, 5G,mMIMO) as discussed hereinafter.

The terms “user equipment,” “UE,” and “user device” are usedinterchangeably to refer to a device employed by an end-user thatcommunicates using a network. UE generally includes one or more antennacoupled to a radio for exchanging (e.g., transmitting and receiving)transmissions with a nearby base station, via an antenna array of thebase station. In embodiments, UE may take on any variety of devices,such as a personal computer, a laptop computer, a tablet, a netbook, amobile phone, a smart phone, a personal digital assistant, a wearabledevice, a fitness tracker, or any other device capable of communicatingusing one or more resources of the network. UE may include componentssuch as software and hardware, a processor, a memory, a displaycomponent, a power supply or power source, a speaker, a touch-inputcomponent, a keyboard, and the like. In embodiments, some of the UEdiscussed herein may include current UE capable of using 5G and havingbackward compatibility with prior access technologies, current UEcapable of using 5G and lacking backward compatibility with prior accesstechnologies, and legacy UE that is not capable of using 5G.

The terms “radio,” “controller,” “antenna,” and “antenna array” are usedinterchangeably to refer to one or more software and hardware componentsthat facilitate sending and receiving wireless radio-frequency signals,for example, based on instructions from a base station. A radio may beused to initiate and generate information that is then sent out throughthe antenna array, for example, where the radio and antenna array may beconnected by one or more physical paths. Generally, an antenna arraycomprises a plurality of individual antenna elements. The antennasdiscussed herein may be dipole antennas, having a length, for example,of ¼, ½, 1, or 1 ½ wavelength. The antennas may be monopole, loop,parabolic, traveling-wave, aperture, yagi-uda, conical spiral, helical,conical, radomes, horn, and/or apertures, or any combination thereof.The antennas may be capable of sending and receiving transmission viaFD-MIMO, Massive MIMO, 3G, 4G, 5G, and/or 802.11 protocols andtechniques.

Additionally, it will be understood that terms such as “first,”“second,” and “third” are used herein for the purposes of clarity indistinguishing between elements or features, but the terms are not usedherein to import, imply, or otherwise limit the relevance, importance,quantity, technological functions, sequence, order, and/or operations ofany element or feature unless specifically and explicitly stated assuch.

FIG. 1 depicts a high-level example of a network environment 100 inaccordance with embodiments of the present disclosure. The networkenvironment 100 is but one example of a suitable network environment andis not intended to suggest any limitation as to the scope of use orfunctionality of the disclosure. Neither should the network environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated.

The network environment 100 may include an alarm management system 102.In some embodiments, the network environment 100 may be atelecommunication network (e.g., a telecommunication network such as,but not limited to, a wireless telecommunication network), or portionthereof. The network environment 100 may include one or more devices andcomponents, such as base stations, servers, switches, relays,amplifiers, databases, nodes, etc. which are not shown so as to notconfuse other aspects of the present disclosure. Those devices andcomponents may provide connectivity in a variety of implementations. Inaddition, the network environment 100 may be utilized in a variety ofmanners, such as a single network, multiple networks, or as a network ofnetworks, but, ultimately, is shown as simplified as possible to avoidthe risk of confusing other aspects of the present disclosure.

The alarm management system 102 may receive alarms from a multitude ofthe nodes 104-134. Each of the nodes 104-134 may be any component of acommunication network, including, for example, an IP router, or acomponent from an evolved packet core, such as a Mobile ManagementEntity (MME), a Telephony Application Server (TAS), a Packet DataNetwork Gateway (PGW), a Home Subscriber Server (HSS), a Policy andCharging Rules Function (PCRF), an Enhanced Serving Mobile LocationCenter (E-SMLC), or a Gateway Mobile Location Center (GMLC), or acomponent from a 5G core (5GC). In other examples, one or more of thenodes 104-134 may be any component of an Enhanced 911 (E911) network,such as a Public Safety Answering Point (PSAP), an Enhanced ServingMobile Location Center (E-SMLC), a Gateway Mobile Location Center(GMLC), an Access & Mobility Management Function (AMF), a LocationManagement Function (LMF), or a Mobile Positioning Center (MPC). One ormore of nodes 104-134 may also be components of a radio access network,such a remote radio head, an eNodeB, a gNodeB, a baseband unit, or amobile switching unit. The network environment 100 may include anycommunication network providing voice and/or data service(s), such as,for example, a 1x circuit voice, a 3G network (e.g., CDMA, CDMA 2000,WCDMA, GSM, UMTS, a 4G network (LTE, WiMAX, HSDPA), 5G, or a 6G network.

Having described network environments 100 and components operatingtherein, it will be understood by those of ordinary skill in the artthat the network environment 100 is but an example of a suitable networkand is not intended to limit the scope of use or functionality ofaspects described herein. Similarly, network environment 100 should notbe interpreted as imputing any dependency and/or any requirements withregard to each component and combination(s) of components illustrated inFIG. 1 . It will be appreciated by those of ordinary skill in the artthat the number, interactions, and physical location of componentsillustrated in FIG. 1 is an example, as other methods, hardware,software, components, and devices for establishing one or morecommunication links between the various components may be utilized inimplementations of the present invention. It will be understood to thoseof ordinary skill in the art that the components may be connected invarious manners, hardwired or wireless, and may use intermediarycomponents that have been omitted or not included in FIG. 1 forsimplicity’s sake. As such, the absence of components from FIG. 1 shouldnot be interpreted as limiting the present invention to excludeadditional components and combination(s) of components. Moreover, thoughcomponents may be represented as singular components or may berepresented in a particular quantity in FIG. 1 it will be appreciatedthat some aspects may include a plurality of devices and/or componentssuch that FIG. 1 should not be considered as limiting the quantity ofany device and/or component.

FIG. 2 illustrates another example of a network environment 200 incertain embodiments. The network environment 200 may include the nodes104-134, the alarm management system 102, a machine learning model 206,and a user interface 208. FIG. 3 illustrates the nodes 104-134 incertain embodiments of a communication network 300. As FIG. 3 depicts,the nodes 104-134 of the communication network 300 may have variousconnections to one another and may be physically distant from oneanother. Referring back to FIG. 2 , in embodiments, the alarm managementsystem 102 may include an engine 204, and the engine 204 may include adescription analyzer 204A. The nodes 104-134 of the network environment200 may send various alarms to the alarm management system 102. Thenumber of alarms may be significant in number and may represent aplurality of incidents within a communication network. In certainembodiments, each incident may elicit an alarm from a node that is aroot cause of the incident and may also elicit alarms from a pluralityof nodes that are symptoms of the root cause of the incident.

In embodiments, alarms sent from the nodes 104-134 in a communicationnetwork to the alarm management system 102 may include description data.In still further embodiments, the description data may be approximately1000 to 2000 characters long and include a description of an event, apriority of the event, and which nodes have been impacted by the event.In other embodiments, alarms sent from the nodes 104-134 to the alarmmanagement system 102 may include description data with one or more ofthe following descriptors: rdbNoMesgFromProb_Major;rdbProbeConn_Critical; LowMemory; probeFilterClientDisconnect_hrr;InterfaceDown; AggregatorDegraded;Syslog_Cisco_LvL_ROUTING-OSPF-5-ADJCHG; cefcPowerStatusChange;bfd_sessions; AggregatorLinkDegraded; AggregatorLinkDown;InterfaceErrors; NodeDown; SNMPLinkUpDownFlapping; ConnectionDown; orMultilink_degraded. The description analyzer 204A of the engine 204 mayperform text mining on the alarms sent from nodes 104-134 to the alarmmanagement system 102 to determine similarities between the receivedalarms. In embodiments, these similarities may be determined using theJaro-Winkler algorithm with Bipartite Matching.

In other embodiments, the alarms sent from the nodes 104-134 to thealarm management system 102 may include description data in the form ofa textual-string identifying the alarm and an Internet Protocol (IP)address. In embodiments, each of these IP addresses may represent the IPaddress of the node sending the alarm and may be converted by the engine204 and/or the description analyzer 204A into the name of the node thatthe address represents. For example, a given IP address may be convertedinto a textual string delineating the name of the node. The descriptionanalyzer 204A of the engine 204 may further perform text mining on thesetextual strings describing the names of the nodes as a further aid indetermining the similarities between alarms. In embodiments, thedescription analyzer 204A of the engine 204, using the description dataincluded in the alarms sent from the nodes 104-134 to the alarmmanagement system 102 may determine a subset of the set of alarmsreceived that represents one of the plurality of incidents.

In certain embodiments, the description analyzer 204A may use the namesof the nodes that have been generated from the received IP addresses todetermine the subset of the set of alarms received that represents oneof the plurality of incidents. For example, the description analyzer204A may use the textual-strings associated with the received set ofalarms and/or the Internet Protocol (IP) addresses included with thereceived set of alarms from the nodes 104-134 to determine that thesubset of alarms representing one incident includes the nodes 106, 108,114, 118, 126, 128, 132, and 134. FIG. 4 illustrates a graphical userinterface depicting a communication network 400 and the nodes 106, 108,114, 118, 126, 128, 132, and 134 representing one incident. As FIG. 4illustrates, network administrators may quickly experience alarmoverload as incidents and the plethora of alarms that they trigger occurin the network.

Referring back to FIG. 2 , once the subset of the set of alarms for anincident has been determined, this subset is provided to the machinelearning model 206. The machine learning model 206 may be any type ofmachine learning model, such as a machine learning model(s) using linearregression, logistic regression, decision trees, support vector machines(SVM), Naive Bayes, k-nearest neighbor (Knn), K means clustering, randomforest, dimensionality reduction algorithms, gradient boostingalgorithms, neural networks (e.g., auto-encoders, convolutional,recurrent, perceptrons, long/short term memory/LSTM, Hopfield,Boltzmann, deep belief, deconvlutional, generative adversarial, liquidstate machine, etc.), and/or other types of machine learning models.

As an example, such as where the machine learning model 206 includes aconvolution neural network (CNN), the CNN may include any number oflayers. One or more of the layers may include an input layer. The inputlayer may hold values associated with the sample data 114 (e.g., beforeor after post-processing). One or more layers may include convolutionallayers. The convolutional layers may compute the output of neurons thatare connected to local regions in an input layer, each neuron computinga dot product between their weights and a small region they areconnected to in the input volume. A result of the convolutional layersmay be another volume, with one of the dimensions based on the number offilters applied (e.g., the width, the height, and the number of filters,such as 32 x 32 x 12, if 12 were the number of filters).

One or more layers may include deconvolutional layers (or transposedconvolutional layers). For example, a result of the deconvolutionallayers may be another volume, with a higher dimensionality than theinput dimensionality of data received at the deconvolutional layer. Oneor more of the layers may include a rectified linear unit (ReLU) layer.The ReLU layer(s) may apply an elementwise activation function, such asthe max (0, x), thresholding at zero, for example. The resulting volumeof a ReLU layer may be the same as the volume of the input of the ReLUlayer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16 x 16 x 12 from the 32 x 32x 12 input volume). One or more of the layers may include one or morefully connected layer(s). Each neuron in the fully connected layer(s)may be connected to each of the neurons in the previous volume. Thefully connected layer may compute class scores, and the resulting volumemay be 1 x 1 x number of classes. In some examples, the CNN may includea fully connected layer(s) such that the output of one or more of thelayers of the CNN may be provided as input to a fully connected layer(s)of the CNN. In some examples, one or more convolutional streams may beimplemented by the CNN(s), and some or all of the convolutional streamsmay include a respective fully connected layer(s).

In some non-limiting embodiments, the CNN(s) may include a series ofconvolutional and max pooling layers to facilitate image featureextraction, followed by multi-scale dilated convolutional andup-sampling layers to facilitate global context feature extraction.Although input layers, convolutional layers, pooling layers, ReLUlayers, and fully connected layers are discussed herein with respect tothe CNN(s), this is not intended to be limiting. For example, additionalor alternative layers may be used in the CNN(s), such as normalizationlayers, SoftMax layers, and/or other layer types.

In embodiments, different orders and numbers of the layers of the CNNmay be used depending on the embodiment. In other words, the order andnumber of layers of the CNN(s) is not limited to any one architecture.In addition, some of the layers may include parameters (e.g., weightsand/or biases), such as the convolutional layers and the fully connectedlayers, while others may not, such as the ReLU layers and poolinglayers. In some examples, the parameters may be learned by the CNN(s)during training. Further, some of the layers may include additionalhyper-parameters (e.g., learning rate, stride, epochs, etc.), such asthe convolutional layers, the fully connected layers, and the poolinglayers, while other layers may not, such as the ReLU layers. Theparameters and hyper-parameters are not to be limited and may differdepending on the embodiment.

In embodiments, the machine learning model 206 may be trained to predicta likelihood that one or more of the alarms of the first subset is aroot cause of the incident. For example, the machine learning model 206may be equipped with one or more weighted lists representing thelikelihood that an alarm is a root cause of an incident. The machinelearning model 206 in certain embodiments may select one or more alarmsof the subset of alarms based at least on the likelihood that the one ormore alarms is the root cause of the incident. For example, inembodiments, the machine learning model may determine that an alarm sentfrom the node 118 to the alarm management system 102 has the highestlikelihood of being the root cause of the other alarms within thesubset. In further embodiments, the machine learning model 206 maycommunicate to the user interface 208 for display the one or more alarmsselected as the likely root cause of the subset of alarms. FIG. 5illustrate a graphical user interface 500 in accordance with certainembodiments. For example, as illustrated in FIG. 5 , once the machinelearning model 206 determines that the alarm generated by node 118 isthe likely root cause of the subset of alarms generated by the nodes106, 110, 114, 118, 126, 128, 132, and 134, the machine learning model206 may communicate for display in the graphical user interface 500 thenode 118. In this way, network administrators may be able to pinpointand more clearly visualize the root cause of a multi-alarm-generatingincident. In other embodiments, the actual problem associated with thenode that is the root cause of an incident is communicated for displayin the graphical user interface 500.

In further embodiments, the root cause of each of the plurality ofincidents is determined. For example, a set of alarms may be receivedfrom a plurality of nodes, representing a plurality of incidents. Foreach incident, an engine may be used to determine a subset of the set ofalarms representing the incident by analyzing description dataassociated with the set of alarms. The subset of the set of alarmsrepresenting each incident may then be applied to a machine learningmodel trained to predict a likelihood that one or more alarms of eachsubset is a root cause of an incident. One or more alarms of each subsetmay be then be selected based at least on the likelihood that the one ormore alarms is the root cause of an incident. The one or more alarmsrepresenting the root cause for each incident may then be communicatedfor display in a graphical user interface.

In still further embodiments, once a root cause for each of a pluralityof incidents has been determined, a shortest path distance between theroot cause of each incident may be computed. In examples, this shortestpath distance may be computed using Dijkstra’s algothrim and may becommunicated for display on the graphical user interface 208 therebyfurther enhancing the visual depiction of the relationship between theroot causes of a plurality of incidents while reducing the risk of alarmfatigue.

In certain embodiments, ground truth data in the form of the actual rootcause of an incident may be used to further train the machine learningmodel 206. For example, where the actual root cause of an incidentdiverges from the root cause predicted by the machine learning model206, the actual root cause of the incident may be used to updateparameters - e.g., weights and biases - of the machine learning model206 using one or more loss functions. In other examples, where theactual root cause of an incident conforms with the root cause predictedby the machine learning model 206, the actual root cause of the incidentmay still be used to update parameters - e.g., weights and biases - ofthe machine learning model 206. Updated parameters in the form of theactual root cause of various incidents may continue to be supplied tothe machine learning model 206 until the machine learning model’s levelof accuracy in predicting a root cause of an incident falls within anacceptable level.

In still further embodiments, ground truth in the form of the actualroot cause for each incident of a plurality of incidents occurringwithin a given time period in a communication network may be used tofurther train the machine learning model 206. For example, where theactual root cause of one or more incidents diverges from the root causepredicted by the machine learning model 206, the actual root cause ofthe one or more incidents may be used to update parameters - e.g.,weights and biases - of the machine learning model 206 using one or moreloss functions. In other examples, where the actual root cause of one ormore incidents conform with the root cause predicted by the machinelearning model 206, the actual root cause of the one or more incidentsmay be used to update parameters - e.g., weights and biases - of themachine learning model 206.

FIG. 6 depicts a flow diagram of an example method 600 for root causeincident detection in a communication network in accordance withimplementations of the present disclosure. The method 600, at block 602,includes receiving alarms. In examples, the alarm management system 102may receive a set of alarms from a plurality of nodes, such as the nodes104-134, representing a plurality of incidents. The method, at block604, includes determining a subset of alarms for an incident descriptiondata. In examples, the description analyzer 204A of the engine 204 ofthe alarm management system 102 may determine a subset of the set ofalarms representing an incident by analyzing description data associatedwith the set of alarms. In embodiments, this description data may betextual strings representing the alarms and IP addresses representingthe nodes, such as the nodes 104-132, which have precipitated thealarms. The method, at block 606, includes applying the subset of alarmsto the machine learning model. For example, the engine 204 or thedescription analyzer 204A may apply the subset of alarms representing anincident to the machine learning model 206. The machine learning model206 may be trained to predict a likelihood that one or more alarms ofthe subset is a root cause of the incident. The method, at block 608,includes selecting an alarm from the subset based on the likelihood thatthe alarm is a root cause of an incident. For example, the machinelearning model 206 may select one or more alarms of the subset of alarmsbased at least on the likelihood that said one or more alarms is theroot cause of the incident. In embodiments, the machine learning model206 may be equipped with one or more weighted lists representing thelikelihood that an alarm is a root cause of an incident. The method, atblock 610, includes communicating the root cause for display. Forexample, the machine learning model 206 or another component of thealarm management system 102 may communicate for display in the graphicaluser interface 208 the root cause of the incident.

Referring to FIG. 7 , a block diagram of an example of a computingdevice 700 suitable for use in implementations of the technologydescribed herein is provided. In particular, the exemplary computerenvironment is shown and designated generally as computing device 700.Computing device 700 is but one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. Neither should computingdevice 700 be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated.

The implementations of the present disclosure may be described in thegeneral context of computer code or machine-useable instructions,including computer-executable instructions such as program components,being executed by a computer or other machine, such as a personal dataassistant or other handheld device. Generally, program components,including routines, programs, objects, components, data structures, andthe like, refer to code that performs particular tasks or implementsparticular abstract data types. Implementations of the presentdisclosure may be practiced in a variety of system configurations,including handheld devices, consumer electronics, general-purposecomputers, specialty computing devices, etc. Implementations of thepresent disclosure may also be practiced in distributed computingenvironments where tasks are performed by remote-processing devices thatare linked through a communications network.

As shown in FIG. 7 , computing device 700 includes a bus 702 thatdirectly or indirectly couples various components together. The bus 702may directly or indirectly one or more of memory 704, processor(s) 706,presentation component(s) 708 (if applicable), input/output (I/O)port(s) 712, input/output (I/O) component(s) 714, and/or power supply716. Although the components of FIG. 7 are shown with lines for the sakeof clarity, in reality, delineating various components is not so clear,and metaphorically, the lines would more accurately be grey and fuzzy.For example, one may consider a presentation component(s) 708 such as adisplay device to be one of I/O components 714. Also, the processor(s)706 may include memory 704, in another example. The present disclosurehereof recognizes that such is the nature of the art, and reiteratesthat FIG. 7 is merely illustrative of an example of a computing device700 that may be used in connection with one or more implementations ofthe present disclosure. Distinction is not made between such categoriesas “workstation,” “server,” “laptop,” “handheld device,” etc., as allare contemplated within the scope of the present disclosure and refer to“computer” or “computing device.”

Memory 704 may take the form of memory components described herein.Thus, further elaboration will not be provided here, but it should benoted that memory 704 may include any type of tangible medium that iscapable of storing information, such as a database or data store. Adatabase or data store may be any collection of records, files, orinformation encoded as electronic data and stored in memory 704, forexample. In one embodiment, memory 704 may include a set of embodiedcomputer-readable and executable instructions that, when executed,facilitate various functions or elements disclosed herein. Theseembodied instructions will variously be referred to as “instructions” oran “application” for short.

Processor(s) 706 may be multiple processors that receive instructionsand process them accordingly. Presentation component(s) 708, ifavailable, may include a display device, an audio device such as aspeaker, and/or other components that may present information throughvisual (e.g., a display, a screen, a lamp (LED), a graphical userinterface (GUI), and/or even lighted keyboards), auditory, and/or othertactile or sensory cues.

A wireless telecommunication network might include an array of devices,which are not shown so as to not obscure more relevant aspects of theinvention. Components such as a base station, a communications tower, oreven access points (as well as other components) can provide wirelessconnectivity in some embodiments.

The input/output (I/O) ports 712 may take a variety of forms. ExemplaryI/O ports 712 may include a USB jack, a stereo jack, an infrared port, afirewire port, other proprietary communications ports, and the like.Input/output (I/O) components 714 may comprise keyboards, microphones,speakers, touchscreens, and/or any other item usable to directly orindirectly input data into the computing device 700.

Power supply 716 may include batteries, fuel cells, and/or any othercomponent that may act as a power source to supply power to thecomputing device 700 or to other network components, including throughone or more electrical connections or couplings. Power supply 716 may beconfigured to selectively supply power to different componentsindependently and/or concurrently.

Finally, regarding FIGS. 1 through 7 , it will be understood by those ofordinary skill in the art that the environment(s), system(s), and/ormethods(s) depicted are not intended to limit the scope of use orfunctionality of the present embodiments. Similarly, the environment(s),system(s), and/or methods(s) should not be interpreted as imputing anydependency and/or any requirements with regard to each component, eachstep, and combination(s) of components or step(s) illustrated therein.It will be appreciated by those having ordinary skill in the art thatthe connections illustrated the figures are contemplated to potentiallyinclude methods, hardware, software, and/or other devices forestablishing a communications link between the components, devices,systems, and/or entities, as may be utilized in implementation of thepresent embodiments. As such, the absence of component(s) and/orsteps(s) from the figures should be not be interpreted as limiting thepresent embodiments to exclude additional component(s) and/orcombination(s) of components. Moreover, though devices and components inthe figures may be represented as singular devices and/or components, itwill be appreciated that some embodiments can include a plurality ofdevices and/or components such that the figures should not be consideredas limiting the number of devices and/or components.

It is noted that aspects of the present invention are described hereinwith reference to block diagrams and flowchart illustrations. However,it should be understood that each block of the block diagrams and/orflowchart illustrations may be implemented in the form of a computerprogram product, an entirely hardware embodiment, a combination ofhardware and computer program products, and/or apparatus, systems,computing devices/entities, computing entities, and/or the like carryingout instructions, operations, steps, and similar words usedinterchangeably (e.g., the executable instructions, instructions forexecution, program code, and/or the like) on a computer-readable storagemedium for execution. For example, retrieval, loading, and execution ofcode may be performed sequentially such that one instruction isretrieved, loaded, and executed at a time. In some embodiments,retrieval, loading, and/or execution may be performed in parallel suchthat multiple instructions are retrieved, loaded, and/or executedtogether. Thus, such embodiments can produce specifically-configuredmachines performing the steps or operations specified in the blockdiagrams and flowchart illustrations. Accordingly, the block diagramsand flowchart illustrations support various combinations of embodimentsfor performing the specified instructions, operations, or steps.

Additionally, as should be appreciated, various embodiments of thepresent disclosure described herein can also be implemented as methods,apparatus, systems, computing devices/entities, computing entities,and/or the like. As such, embodiments of the present disclosure can takethe form of an apparatus, system, computing device, computing entity,and/or the like executing instructions stored on a computer-readablestorage medium to perform certain steps or operations. However,embodiments of the present disclosure can also take the form of anentirely hardware embodiment performing certain steps or operations.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the scopeof the claims below. Embodiments of our technology have been describedwith the intent to be illustrative rather than restrictive. Alternativeembodiments will become apparent to readers of this disclosure after andbecause of reading it. Alternative means of implementing theaforementioned may be completed without departing from the scope of theclaims below. Certain features and subcombinations are of utility andmay be employed without reference to other features and subcombinationsand are contemplated within the scope of the claims.

1. A method in a communication network, the method comprising: receivinga set of alarms from a plurality of nodes, the set of alarmsrepresenting a plurality of incidents; analyzing description dataassociated with the set of alarms; based on the analyzing, determining,using an engine, a plurality of subsets of the set of alarms, eachsubset representing an incident of the plurality of incidents;determining, using a machine learning model, likelihoods that alarms ofeach subset of the plurality of subsets are root causes of incidents ofthe plurality of incidents; selecting one or more alarms of the set ofalarms based at least on the likelihoods that said one or more alarmsare the root causes of the incidents; determining a shortest pathdistance between the one or more alarms; and communicating, for displayin a graphical user interface, the one or more alarms and the shortestpath distance between the one or more alarms.
 2. The method of claim 1,wherein the description data associated with the set of alarms isgenerated based at least in part by converting a plurality of InternetProtocol (IP) addresses to a plurality of node names.
 3. The method ofclaim 1, wherein the root causes are associated with nodes of theplurality of nodes.
 4. (canceled)
 5. (canceled)
 6. A system in acommunication network, the system comprising: one or more processors;and one or more computer storage hardware devices storingcomputer-usable instructions that, when used by the one or moreprocessors, cause the one or more processors to: receive a set of alarmsfrom a plurality of nodes, the set of alarms representing a plurality ofincidents; analyze description data associate with the set of alarms;based on the analysis, determine a first plurality of subsets of the setof alarms, each subset representing an incident of the plurality ofincidents; determine, using a machine learning model, likelihoods thatalarms of each subset of the plurality of subsets are root causes ofincidents of the plurality of incidents; select one or more alarms ofthe set of alarms based at least on the likelihoods that said one ormore alarms are the root causes of the incidents; determine a shortestpath distance between the one or more alarms; and communicate, fordisplay in a graphical user interface, the one or more alarms and theshortest path distance between the one or more alarms.
 7. The system ofclaim 6, wherein the description data associated with the set of alarmsis generated in part by converting a plurality of Internet Protocol (IP)addresses to a plurality of node names.
 8. The system of claim 6,wherein the root causes are a nodes of the plurality of nodes.
 9. Thesystem of claim 6, wherein the root causes are a problems associatedwith nodes of the plurality of nodes.
 10. A method in a communicationnetwork, the method comprising: receiving a set of alarms from aplurality of nodes, the set of alarms representing a plurality ofincidents; analyzing description data associated with the set of alarms:based on the analyzing, determining a subset of the set of alarmsrepresenting a first incident of the plurality of incidents; generating,by applying the subset to a machine learning model, one or morepredictions regarding a first root cause of the first incident;selecting a first alarm of the subset based at least on the one or morepredictions; determining a shortest path distance between the firstalarm and a second alarm; and communicating, for display on a graphicuser interface, the shortest path distance between the first alarm andthe second alarm.
 11. The method of claim 10, wherein the descriptiondata associated with the set of alarms is generated in part byconverting a plurality of Internet Protocol (IP) addresses to aplurality of node names.
 12. The method of claim 10, wherein the firstroot cause is a root cause device.
 13. The method of claim 10, whereinthe first root cause is a problem associated with a root cause device.14. (canceled)
 15. The method of claim 10, wherein the set of alarmscomprises the second alarm.
 16. The method of claim 10, furthercomprising selecting the second alarm based on a prediction by themachine learning model that the second alarm is a second root cause of asecond incident of the plurality of incidents.
 17. The method of claim10, further comprising updating parameters of the machine learning modelbased on an actual root cause of the first incident.